This invention generally relates to compressing spatiotemporal data.
Large volumes of spatiotemporal data are being generated by moving objects (e.g., the connected cars initiative). A key challenge with this data is to simultaneously support: (i) high spatial update rates to handle moving objects, and (ii) high query rates to answer spatial queries (e.g., when an accident happens, notify nearby cars). In order to sustain high query rates, traditional solutions index inputs (and updates) using a spatial index (e.g., DB2 grid index, Informix r-tree index, etc.).
The rate at which a database can support updates depends on the nature of indexing; generally a no-index update is faster than a hash map update (equality query) followed by a b-tree index update (for range queries) followed by a r-tree/grid index update (for spatial queries). The rate of updates (even today and more so in the near projected future) is above the rate at which commercial databases can handle spatial updates (i.e., insert and update spatial index).